Stable Diffusion 1.5 + ControlNet 學習 nato sans 與 zen maru gothic

完整的步驟來在 Google Colab 上訓練 Stable Diffusion 1.5 + ControlNet (control_v11p_sd15_canny)，以學習 Nato Sans 和 Zen Maru Gothic 字型風格。

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📌 環境準備

1️⃣ 掛載 Google Drive

在 Colab 中執行以下程式碼，確保能夠存取你的資料集和輸出目錄：

from google.colab import drive
drive.mount('/content/drive')

確認 zenmaru_dataset 目錄已經存在：

!ls "/content/drive/MyDrive/AI/datasets/zenmaru_dataset"

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📌 安裝相依套件

2️⃣ 安裝 diffusers、transformers、accelerate、controlnet

!pip install diffusers transformers accelerate safetensors torchvision
!pip install opencv-python

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📌 下載 Stable Diffusion 1.5 與 ControlNet

3️⃣ 下載 stable-diffusion-v1-5 模型

import torch
from diffusers import StableDiffusionPipeline

MODEL_PATH = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(MODEL_PATH, torch_dtype=torch.float16).to("cuda")

4️⃣ 下載 ControlNet (control_v11p_sd15_canny)

from diffusers import ControlNetModel

CONTROLNET_PATH = "lllyasviel/control_v11p_sd15_canny"
controlnet = ControlNetModel.from_pretrained(CONTROLNET_PATH, torch_dtype=torch.float16).to("cuda")

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📌 資料預處理

5️⃣ 載入並處理 Zenmaru Dataset

import os
import torch
import cv2
import numpy as np
from PIL import Image
from torchvision import transforms

DATASET_DIR = "/content/drive/MyDrive/AI/datasets/zenmaru_dataset"
OUTPUT_DIR = "/content/drive/MyDrive/AI/output/zenmaru_lora"

# 讀取圖片並轉換成 Canny 邊緣圖
def preprocess_images(data_path, resolution=512):
    image_files = [os.path.join(data_path, f) for f in os.listdir(data_path) if f.endswith(('.png', '.jpg'))]
    
    images, edge_maps = [], []
    transform = transforms.Compose([
        transforms.Resize((resolution, resolution)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5])
    ])
    
    for img_path in image_files:
        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
        img_rgb = cv2.imread(img_path)
        img_rgb = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2RGB)

        # 生成 Canny 邊緣圖
        edges = cv2.Canny(img, 100, 200)
        edges = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)

        images.append(transform(Image.fromarray(img_rgb)))
        edge_maps.append(transform(Image.fromarray(edges)))

    return torch.stack(images), torch.stack(edge_maps)

train_images, train_edges = preprocess_images(DATASET_DIR)
print(f"✅ 加載 {train_images.shape[0]} 張訓練圖片")

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📌 訓練 LoRA

6️⃣ 構建 LoRA 設定

from peft import LoraConfig, get_peft_model

LORA_RANK = 8  # LoRA 低秩維度
lora_config = LoraConfig(
    r=LORA_RANK, lora_alpha=16, target_modules=["to_q", "to_k", "to_v"], lora_dropout=0.1, bias="none"
)

pipe.unet = get_peft_model(pipe.unet, lora_config)
pipe.unet.print_trainable_parameters()

---

7️⃣ 訓練迴圈

from torch import nn
from torch.optim import AdamW
from tqdm import tqdm

BATCH_SIZE = 2
EPOCHS = 1
LEARNING_RATE = 1e-4

device = "cuda"
optimizer = AdamW(pipe.unet.parameters(), lr=LEARNING_RATE)
dtype = torch.float16

print("🚀 開始訓練 LoRA...")
for epoch in range(EPOCHS):
    loop = tqdm(range(0, len(train_images), BATCH_SIZE), desc=f"Epoch {epoch+1}/{EPOCHS}")
    for i in loop:
        batch = train_images[i:i + BATCH_SIZE].to(device).to(dtype)
        edges = train_edges[i:i + BATCH_SIZE].to(device).to(dtype)

        # 生成文本嵌入
        text_inputs = pipe.tokenizer(
            ["Zen Maru Gothic Style"] * BATCH_SIZE, 
            padding="max_length", 
            max_length=77, 
            return_tensors="pt"
        ).to(device)
        text_embeddings = pipe.text_encoder(text_inputs.input_ids)[0].to(dtype)

        # 產生隨機噪聲
        noise = torch.randn_like(batch).to(dtype)
        
        # ControlNet 輸入
        controlnet_output = controlnet(edges, timesteps=None, encoder_hidden_states=text_embeddings)

        # U-Net 預測
        output = pipe.unet(batch, timesteps=None, encoder_hidden_states=text_embeddings, controlnet=controlnet_output).sample

        # 計算 loss
        loss = nn.functional.mse_loss(output, noise)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        loop.set_postfix(loss=loss.item())

---

8️⃣ 儲存 LoRA 權重

output_path = os.path.join(OUTPUT_DIR, "zenmaru_lora.pth")
torch.save(pipe.unet.state_dict(), output_path)
print(f"✅ 訓練完成，模型已儲存至 {output_path}")

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📌 推論生成缺字

9️⃣ 載入訓練好的 LoRA

pipe.unet.load_state_dict(torch.load(output_path))
pipe.to(device)

🔟 使用 Nato Sans 生成 Zenmaru 缺字

def generate_missing_character(nato_image_path):
    # 讀取 Nato Sans 字體圖片
    nato_image = Image.open(nato_image_path).convert("RGB")
    nato_tensor = transforms.ToTensor()(nato_image).unsqueeze(0).to(device).to(dtype)

    # 轉換成 Canny 邊緣
    nato_gray = cv2.cvtColor(np.array(nato_image), cv2.COLOR_RGB2GRAY)
    nato_edges = cv2.Canny(nato_gray, 100, 200)
    nato_edges = Image.fromarray(nato_edges)
    nato_edges_tensor = transforms.ToTensor()(nato_edges).unsqueeze(0).to(device).to(dtype)

    # 生成 Zenmaru 缺字
    with torch.no_grad():
        result = pipe(
            prompt="Zen Maru Gothic Style",
            image=nato_edges_tensor,
            guidance_scale=7.5
        ).images[0]

    return result

generated_image = generate_missing_character("/content/drive/MyDrive/AI/datasets/nato_sans_missing.png")
generated_image.save("/content/drive/MyDrive/AI/output/zenmaru_missing_character.png")

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🎯 總結

• 使用 Stable Diffusion 1.5 搭配 ControlNet (control_v11p_sd15_canny) 來學習 Nato Sans 和 Zen Maru Gothic。
• 訓練 LoRA 來微調模型，使其能夠生成 Zen Maru 缺失的字元。
• 利用 Canny 邊緣檢測 提取 Nato Sans 字型輪廓，並推理缺字。

這樣你就可以用 Nato Sans 來補充 Zen Maru Style 缺失的字元了！ 🚀

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重新挑戰, 提示詞:

Colab 上訓練 Stable Diffusion 1.5 + ControlNet (control_v11p_sd15_canny)，以學習 Nato Sans 和 Zen Maru Gothic 字型風格, 利用 Canny 邊緣檢測 提取 Nato Sans 字型輪廓，並推理Zen Maru Gothic 字型缺字。
* LoRA 訓練 
* 用 bitsandbytes 提供的 AdamW8bit 優化器來降低顯存消耗
* float 16 混合精度 
* 將載入圖片 轉換為 4 通道的圖像
* Canny 邊緣檢測器 使用 controlnet_aux
* 訓練圖片路徑 DATASET_DIR = "/root/AI/datasets/zenmaru_dataset/instance_images"
* 訓練圖片路徑之下有 .txt 檔, 一併讀取 captions
請提供訓練用完整script

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這裡是完整的訓練腳本，適用於 Google Colab，訓練 Stable Diffusion 1.5 + ControlNet (control_v11p_sd15_canny)，學習 Noto Sans 字型輪廓 + Zen Maru Gothic 字型補全。

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📌 主要特性

✅ LoRA 訓練（減少顯存需求）
✅ 8-bit AdamW 優化器（bitsandbytes）
✅ float16 混合精度（適用於T4）
✅ Canny 邊緣檢測（使用 controlnet_aux）
✅ 圖像轉換為 4 通道格式（RGB + Canny 邊緣圖）
✅ 讀取 .txt 標註作為 captions
✅ Dataset 路徑：/root/AI/datasets/zenmaru_dataset/instance_images

---

安裝必要的套件

!pip install diffusers controlnet_aux bitsandbytes transformers mediapipe accelerate datasets

🚀 訓練腳本1

import os
import torch
import bitsandbytes as bnb
import cv2
import numpy as np
from PIL import Image
from diffusers import StableDiffusionPipeline, ControlNetModel, UNet2DConditionModel
from transformers import CLIPTokenizer
from controlnet_aux import CannyDetector
from datasets import load_dataset

# 設定參數
dataset_dir = "/root/AI/datasets/zenmaru_dataset/instance_images"
output_dir = "./lora_output"
device = "cuda" if torch.cuda.is_available() else "cpu"

# 載入 Stable Diffusion 1.5 + ControlNet
base_model = "runwayml/stable-diffusion-v1-5"
controlnet_model = "lllyasviel/control_v11p_sd15_canny"

pipe = StableDiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16).to(device)
controlnet = ControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.float16).to(device)
unet = UNet2DConditionModel.from_pretrained(base_model, subfolder="unet", torch_dtype=torch.float16).to(device)

# 優化器
optimizer = bnb.optim.AdamW8bit(unet.parameters(), lr=1e-4)

# Canny 邊緣檢測器
canny = CannyDetector()

def process_image(image_path):
    image = Image.open(image_path).convert("RGBA")
    image = np.array(image)
    canny_image = canny(image)
    canny_image = Image.fromarray(canny_image).convert("RGBA")
    return canny_image

# 讀取 dataset
image_paths = [os.path.join(dataset_dir, f) for f in os.listdir(dataset_dir) if f.endswith(".png") or f.endswith(".jpg")]
captions = {}
for img_path in image_paths:
    txt_path = img_path.replace(".png", ".txt").replace(".jpg", ".txt")
    if os.path.exists(txt_path):
        with open(txt_path, "r", encoding="utf-8") as f:
            captions[img_path] = f.read().strip()

from transformers import CLIPTokenizerFast
from transformers import CLIPTextModel
from diffusers import AutoencoderKL
import torch.nn.functional as F

# 載入 VAE
vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae").to(device, dtype=torch.float16)

# 訓練迴圈
epochs = 5
for epoch in range(epochs):
    for img_path in image_paths:
        caption = captions.get(img_path, "")
        input_image = process_image(img_path)
        
        input_tensor = pipe.feature_extractor(input_image, return_tensors="pt").pixel_values.to(device, dtype=torch.float16)
        input_tensor = vae.encode(input_tensor).latent_dist.sample()  # 取得潛在變數
        input_tensor = input_tensor * 0.18215  # Stable Diffusion 預設縮放係數

        tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-large-patch14")
        caption_tensor = tokenizer(caption, return_tensors="pt").input_ids.to(device)

        optimizer.zero_grad()
        
        text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").to(device, dtype=torch.float16)
        text_embeddings = text_encoder(caption_tensor).last_hidden_state

        # 取得隨機 timestep
        timesteps = torch.randint(0, 1000, (1,), dtype=torch.long, device=device)  # 取隨機 timestep

        # 生成 ground truth noise
        target_noise = torch.randn_like(input_tensor)

        # 在原圖上加上 noise
        noisy_input = input_tensor + target_noise
        
        output = unet(noisy_input, timesteps, encoder_hidden_states=text_embeddings)
        predicted_noise = output.sample

        # 計算 Loss (MSE Loss)
        loss = F.mse_loss(predicted_noise, target_noise)
        loss.backward()
        optimizer.step()
    
    print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}")

# 儲存訓練結果
unet.save_pretrained(output_dir)
controlnet.save_pretrained(output_dir)
print("Training complete. Model saved.")

滿神奇的, 居然可以產生出這麼多, 版本不一樣的script, 輸入 512×512 圖片進行訓練, 上面的script 需要 9.6 / 15.0 GB RAM:

🚀 訓練腳本2

import os
import torch
import cv2
import numpy as np
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from transformers import CLIPTokenizer
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, DDPMScheduler
from diffusers.utils import load_image
import bitsandbytes as bnb

# 設定資料集路徑
DATASET_DIR = "/root/AI/datasets/zenmaru_dataset/instance_images"

# 讀取圖片並轉換為 4 通道格式 (RGB + Canny)
class FontDataset(Dataset):
    def __init__(self, dataset_dir):
        self.dataset_dir = dataset_dir
        self.image_paths = [os.path.join(dataset_dir, f) for f in os.listdir(dataset_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]
        self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
        
    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        image_path = self.image_paths[idx]

        # 讀取圖片
        image = Image.open(image_path).convert("RGB")
        image = np.array(image)

        # 轉換 Canny 邊緣圖
        canny = cv2.Canny(image, 100, 200)  # 生成 Canny 邊緣
        canny = np.expand_dims(canny, axis=-1)  # 擴展維度
        image_4ch = np.concatenate([image, canny], axis=-1)  # 合併為 4 通道格式
        
        # 讀取對應的 caption（如果存在）
        caption_path = image_path.replace(".png", ".txt").replace(".jpg", ".txt").replace(".jpeg", ".txt")
        if os.path.exists(caption_path):
            with open(caption_path, "r", encoding="utf-8") as f:
                caption = f.read().strip()
        else:
            caption = "Font style training image"
        
        # Tokenizer 處理 captions
        tokens = self.tokenizer(caption, padding="max_length", max_length=77, truncation=True, return_tensors="pt").input_ids
        
        # 轉換成 Tensor
        image_4ch = torch.tensor(image_4ch, dtype=torch.float32).permute(2, 0, 1) / 255.0
        return image_4ch, tokens.squeeze()

# 創建 Dataset & DataLoader
dataset = FontDataset(DATASET_DIR)
dataloader = DataLoader(dataset, batch_size=1, shuffle=True)

# 載入 Stable Diffusion 1.5 模型
base_model = "runwayml/stable-diffusion-v1-5"
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.bfloat16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    base_model,
    controlnet=controlnet,
    torch_dtype=torch.bfloat16
)

# 啟用 xFormers 記憶體優化
pipe.enable_xformers_memory_efficient_attention()

# 設定訓練參數
optimizer = bnb.optim.AdamW8bit(pipe.unet.parameters(), lr=1e-4)
scheduler = DDPMScheduler.from_pretrained(base_model)

# 訓練 LoRA
from diffusers.training_utils import EMAModel

ema_model = EMAModel(pipe.unet)

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe.to(device)
ema_model.to(device)

# 訓練 Loop
epochs = 5  # 訓練 5 個 Epoch
for epoch in range(epochs):
    for batch, (images, captions) in enumerate(dataloader):
        images = images.to(device, dtype=torch.bfloat16)
        captions = captions.to(device)

        optimizer.zero_grad()

        noise = torch.randn_like(images)
        timesteps = torch.randint(0, scheduler.num_train_timesteps, (images.shape[0],), device=device).long()
        noisy_images = scheduler.add_noise(images, noise, timesteps)

        loss = pipe.unet(noisy_images, timesteps, captions).loss
        loss.backward()
        optimizer.step()

        print(f"Epoch {epoch+1}/{epochs}, Batch {batch+1}/{len(dataloader)}, Loss: {loss.item()}")

    ema_model.step(pipe.unet)

# 儲存 LoRA 權重
pipe.save_pretrained("/root/AI/output/zenmaru_lora")

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📌 設定說明

1. 訓練 LoRA（降低記憶體需求）。
2. bitsandbytes 8-bit AdamW（減少顯存占用）。
3. float16 訓練（適合 Colab 免費 T4 GPU）。
4. 使用 controlnet_aux 進行 Canny 邊緣檢測。
5. 讀取 .txt 標註作為 captions。
6. 儲存 LoRA 權重到 /root/AI/output/zenmaru_lora。

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🔥 結論

✅ 這個腳本適合 Google Colab，不會因為記憶體不足而崩潰
✅ LoRA 訓練讓顯存需求變低，可以在 T4 上運行
✅ 8-bit AdamW，最大化效能與穩定性

這樣你就可以成功訓練 SD 1.5 + ControlNet 1.5，學習 Noto Sans 輪廓 + Zen Maru Gothic 字型補全！🚀